Soil geography and land evaluation¶
Soil geographic databases¶
An attempt to catalogue all freely-downloadable primary soils information usable in a GIS. Some of the linked sites also have interpreted information.
“We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by Predictive Soil Mapping (PSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales, and compare them to spatial patterns known from detailed field surveys (gSSURGO).”
Case Studies of four areas in the USA as ISRIC Report 2021/03
Access ISRIC’s WoSIS Soil Profile Database, using R Markdown scripts. WoSIS is explained in this paper (DOI: https://doi.org/10.5194/essd-9-1-2017)
This is an exercise and assignment on “Big Data” developed for Cornell course PLSCS/NTRES 6200: Spatial Modelling and Analysis. The first part introduces Google Earth Engine (GEE) with a simple remote sensing example. The second part shows how to process ISRIC’s SoilGrids in GEE. The example is a Principal Components Analysis (PCA) of the six depth slices for a single property.
Using SoilGrids and Google Earth Engine with R to identify “homogeneous” soil zones
Use this script is to identify relatively homogeneous areas with similar soil properties, based on ISRIC’s SoilGrids. These can be used as a pre-map for field work, or for stratified sampling. The homogeneous zones are identified by standardized Principal Components Analysis (PCA) of the stack of SoilGrids soil properties, accessed via Google Earth Engine (GEE). The analysis is also performed in GEE.
To run this script you must first set up R to access Google Earth Engine, as explained in Using Google Earth Engine with R
Processing the Harmonized World Soil Database (Version 2.0) in R
The HWSD v2 is a harmonized map and linked attribute table of the World’s soils, at a nominal scale of 1:1M. It is intended for global or regional modelling: “Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change”. This semi-expert approach, based on legacy maps and expert judgement, has been somewhat superseded by ISRIC’s SoilGrids 250m resolution raster layers. Version 2 is improved, with seven soil layers to 2 m depth and with a consistent soil classification system (WRB 2020) to name map units and their components.
Processing the Harmonized World Soil Database (Version 1.2) in R
Video: ISRIC-World Soil Information: providing consistent information on the world’s soils (Cornell seminar, 25-Jan-2020)
My introduction to ISRIC-World Soil Information
Presentation: Three thoughts on soil class maps: (1) Evaluating classification accuracy (2) Taxonomic vs. geographic soil classes (3) Soil geoforms & phenoforms (Cornell seminar, 22-March-2018)
Video: Can citizen science be used to support digital soil mapping? (Cornell seminar, 18-Mar-2016)
Based on this paper: 10.1016/j.geoderma.2015.05.006
Video: GSIF course: Digital soil resource inventories stutus and prospects (ISRIC Spring School, 3-Aug-2015)
Somewhat outdated but interesting history. Based on this book chapter: 10.1007/978-981-10-0415-5_22
Representative Fraction; Maximum Location Accuracy; Minimum Legible Area; Maximum/Minimum number of field observations; Grid resolution
Land suitability evaluation¶
Rossiter, DG (2003) Biophysical models in land evaluation. Article 1.5.27 in Theme 1.5 Land Use and Land Cover, Verheye, W.H. (ed.), in Encyclopedia of Life Support Systems (EOLSS), developed under the auspices of the UNESCO. Oxford: EOLSS Publishers.
Cornell course: SCAS 494: Land Suitability Evaluation
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. DOI: 10.5194/soil-7-217-2021
Liu, F., Rossiter, D. G., Zhang, G.-L., & Li, D.-C. (2020). A soil colour map of China. Geoderma, 379, 114556. DOI: 10.1016/j.geoderma.2020.114556
Rasaei, Z., Rossiter, D. G., & Farshad, A. (2020). Rescue and renewal of legacy soil resource inventories in Iran as an input to digital soil mapping. Geoderma Regional, e00262. DOI: 10.1016/j.geodrs.2020.e00262
Rossiter, D. G., Zeng, R., & Zhang, G.-L. (2017). Accounting for taxonomic distance in accuracy assessment of soil class predictions. Geoderma, 292, 118–127. DOI: 10.1016/j.geoderma.2017.01.012
Rossiter, D. G., & Bouma, J. (2018). A new look at soil phenoforms - Definition, identification, mapping. Geoderma, 314, 113–121. DOI:10.1016/j.geoderma.2017.11.002
Forbes, T. R., Rossiter, D. G., & Van Wambeke, A R. (1982). Guidelines for evaluating the adequacy of soil resources inventories, SMSS Technical Monograph No. 4. Soil Management Support Service, USDA/Soil Conservation Service, Washington, DC. 50pp.
Somewhat outdated but perhaps a useful introduction to traditional soil survey concepts. If you can find it, Dent, D., & Young, A. (1981). Soil survey and land evaluation. George Allen & Unwin, ISBN 978-0-04-631013-4 is a more formal introduction to this topic.
El anterior, traducido y adaptado al Español por Ronald Vargas Rojas, Universidad Mayor de San Simón, Bolivia (2004)
El anterior, traducido y adaptado al Español por Ronald Vargas Rojas, Universidad Mayor de San Simón, Bolivia y FAO-Proyecto SWALIM (2006)
Atkilt Girma; Rossiter, D. G.; Siderius, W; & Henneman, R. (2001). Soils of the Lake Naivasha area, Kenya: Summary of investigations by the Soil Science Division, ITC , Technical Report ITC Soil Science Division, Enschede, NL, July 2001
Atkilt Girma & Rossiter, D. G. (2001). Soil investigations on the Sulmac farm, Naivasha, Kenya by the Soil Science Division, ITC, Technical Report ITC Soil Science Division, Enschede, NL, July 2001
Husnjak, S., Rossiter, D.G., Hengl, T., & Miloš, B. (2004) Soil inventory and soil classification in Croatia: historical review, current activities, future directions
Cambule, A. H. (Armindo); Rossiter, D. G. (David) (2013). Limpopo National Park (Mozambique) Soil Organic Carbon study. University of Twente. Dataset. doi: 10.4121/uuid:6cb98f84-f0de-47d4-8a2c-d6aaeaf5db08
Data source for DOI: 10.1016/j.geoderma.2013.07.015
Liu, Feng; Rossiter, D. G.; Zhang, Gan-Lin, Li, De-Cheng. (2020). Predicted colours of Chinese soils. doi:10.11666/00072.ver1.db
Maps produced as described in DOI:10.1016/j.geoderma.2020.114556
A computer program that allows land evaluators to build expert systems to evaluate land according to the method presented in the Food and Agriculture Organization’s “Framework for Land Evaluation” (1976) and subsequent Guidelines. Last updated 1996, runs under Microsoft MS-DOS v2.3 or higher, and under MS-DOS emulators.
Simulates subdivisions of USDA Soil Taxonomy soil moisture & temperature regimes for well-drained soils, from a time-sequence of monthly climate data. These pages also have links to more modern methods.
Riha, Susan J., Rossiter, DG, and Simoens, Patrick (1994). GAPS: General-purpose Atmosphere-Plant-Soil Simulator. Version 3.0 User’s Manual. Ithaca, NY: Cornell University Department of Soil, Crop & Atmospheric Sciences. 195pp.
This program has since become a Windows program (sorry I can’t find a link) but this manual explains the principles of the simulation models.
Last modified 17-June-2023